**3.2. The OSE calculator and DIVOM method**

A three-step process was utilized with The OSE Calculator to score the metrics for the calculation of the OSE Rating; (1) Specify the *Validation, Operation* and *Maintenance* customer requirements, (2) determine the appropriate level of *Integration* (3) ensure that the *Design* of the EPP is correct. The metrics are evaluated by examining each component's Attributes in turn (from

Designing The OSE Rating and The Calculator in this fashion significantly increased the potential of conveying a large amount of specialized requirements to a general audience in an extremely short time period and providing five key metrics and an overall KPI to enable a Six

Even though the 5, 3, 10 formats of Metric, Component and Attribute creates a uniformity to reduce the cognitive load it introduces a constraint which although not immediately apparent may be problematic in some situations. The constraint is that the Attributes in The OSE Calculator may not be applicable in every situation, thus there is a requirement for the facilitator to state this as they navigate through the process. Another observation was that the participants frequently wanted to "score high". When these two items are combined they frequently attempt to utilize various justifications to claim that critical Attributes are not applicable to

The experiments outlined in the following sections were conducted to validate that *The ALIZA Tools* have a suitable *"Form"* to reliably deliver the required *"Function"* and seamlessly *"Fit"*

Regarding *Fit*, it is no stretch of the imagination to state that the scorecard methodology *"fits like a glove"* in the overall ALIZA process. It enables the benchmarking processes to rapidly define the current and target situations from a business perspective and utilize a relatively

But the *Form* and *Function* are a totally different matter. Most of the Business Users, after a cursory initial inspection, jumped to the conclusion that the I4-PS and I4-ES scorecards are in an ideal form. The fact that the scorecards clearly outline the key metrics, which have been defined by a reputable industry organization (VDMA), the graphics are easy to understand, they have a continuum and they are measurable appealed very strongly to them. One even went so far as to declare *"Great, now we can manage Industry 4.0".* But this work has delved a lot deeper and found issues with the function which, although not insurmountable, are not

An experiment was designed to validate the accuracy of these scorecards. The objective was to determine if they were *Repeatable* (the same inspector getting the same result when

00 to 10) to determine which Attributes will be achieved (see **Figure 4**).

them. In this scenario strong leadership skills by the Facilitator are required.

Sigma approach to the EPP.

68 New Trends in Industrial Automation

**3. Validation of the ALIZA tools**

seamless interface to The OSE Calculator.

insignificant and must be addressed before widespread adoption.

into *The ALIZA Process*.

**3.1. The scorecards method**

The first stage of the validation of The OSE Calculator and DIVOM Method focused on four industrial EPPs from 2012 to 2016. During these EPPs the researcher performed a DIVOM assessment and facilitated OSE Optimization sessions which evaluated how useful the participants found the overall tools and process. Informal interview and data capture techniques were utilized throughout these sessions.

The case studies clearly demonstrated that DIVOM benchmarking process achieved its *Function* of delivering tangible business benefits in the form of a Data Driven FAT, increased OEE and improved regulatory compliance, but with two strict provisos; the Project Sponsor must be a Change Agent focused on Industry 4.0 (Case Study 1 and 4). If the Project Sponsor is not empowered to enact change (Case Study 3) or is a diehard I3-EPP supporter (Case Study 2), then these methods are worthless and should not be utilized. Even though general awareness of I4 should have progressed since the recommendations were published [22], this work has uncovered underlying inhibiting factors which must be addressed.

Most specialists, observed during these case studies, were unwilling to gain an understating of an Attribute which they felt was not part of their primary discipline. It appears they were intimidated by having to admit that they needed to learn about these Attributes. They were *"the teachers"* not the *"students"*. They were extremely quick to disown these Attributes and assign them to other disciplines without personally gaining any knowledge. Even though it is outside the scope of this stage of the research, this reaction presents an insurmountable barrier to transdisciplinary [23] collaboration and must be better understood if I4 is to succeed in an efficient fashion.

Bloom's Taxonomy [24] they could easily be tested with tools such as Moodle quizzes and the student provided with a novice level certification prior to attendance. This has the potential to create a process which transcends discipline and the collaboration issues which they cause. In this scenario the *Trans-Attribute* collaboration can be enabled where *team members must be competent enough in their own Attributes and understand the language of all relevant Attributes that enables them to contribute to the members' quality research or learning and combine various perspec-*

Industry 3.0 to Industry 4.0: Exploring the Transition http://dx.doi.org/10.5772/intechopen.80347 71

This stage of the research clearly highlights that if true understanding of the Attributes is required a significant amount of time must be invested (second and third semester) to achieve the 70-20-10 rule model [25] for learning. The dialog between a tutor and student, involving several of the common alternatives is also required to produce significantly more understanding [26] than a simplistic exposition of the correct information [27]. But that is only to be

Regarding *Fit*, gloves normally come in pairs and The OSE Calculator and DIVOM Method is the second glove which compliments the first; the Scorecard Methodology. As with gloves, one is of limited use and the whole (The ALIZA Process) it much greater than the sum of the

The ALIZA Canvas, Process and Tools for I4 Manufacturing Equipment represents a significant output of this work, but an educational mechanism is required to rapidly disseminate these tools and methods to derive tangible benefit of industry. This section explores conventional academic educational structures and concludes that an additional, complimentary, structure for *inventing and implement technical solutions, for business problems, in the I4 equipment domain* is required. To that end, the E-Cubers organization has been created with the following objective: *E-Cubers is an educational organization consisting of a constellation of Communities of Practice (CoPs) organized around topics which are designed to facilitate collaboration and creativity for the advancement of each members individual competencies to support the achievement of I4 Equipment* 

Designing and implementing a constellation of CoPs is not trivial matter. In fact, it is fraught with difficulty, but the benefits can be enormous [29]. E-Cubers are only at the start of this exciting journey of exploring how the CoPs can be organized to be truly effective Knowledge Management Systems promoting effective and productive collaboration in the Industry 4.0 Equipment domain. It should not be assumed that CoPs in isolation can guarantee the creativity required for the invention of novel solutions in Industry 4.0. But what is creativity? How can it be nurtured? By examining the applicability of Resnick's Four Ps [30] for cultivating creativity, in the general sense, and refining it to the E-Cubers specific requirements this work

parts (Scorecard Methodology) + (The OSE Calculator and DIVOM Method).

*tives to build up a new framework* [23].

**4. Design of E-Cubers**

*Engineering Excellence.*

has defined the E-Cubers Eight Ps for cultivating creativity.

**4.1. Introduction**

expected on the journey from Novice to Expert [28].

The second stage of the validation of OSE Calculator and DIVOM Method focused on the 2016 and 2017 MEng in Mechatronics at the University of Limerick. The objective during this stage was to determine if the *Form* of the DIVOM process was suitable. A high degree of confidence had been gained from the case studies that the form of The OSE Calculator was fit for purpose in the hands of skilled facilitator, but the question which had to be answered was if others could be trained to be confident Facilitators? This stage did not focus on measuring the absolute accuracy of the student's knowledge because of the risk of bias based on association with academic grading. Instead the students were requested to estimate their own level of understanding to determine their *"confidence"* level. This assumes that any inaccuracies could be minimized based on further training if required.

The same academic format was utilized in the 2016 and 2017 classes. The students were not given access to The OSE Calculator at the outset. They were provided with the Attributes grouped by Component and Metric in a Microsoft Excel Workbook. The 2017 students were provided with a Microsoft Word Document containing explicit requirements for each Attribute at the start of the year, while the 2016 students were not provided with the explicit requirements. In the first semester the theory behind the DIVOM Metrics, Components and Attributes were explained and the students were mentored as groups to perform a DIVOM assessment on the group EPP. In the second semester they worked individually to complete the design of their solution as part of the group EPP, while in the third semester they executed the group EPP. At the end of each semester every student was requested to estimate their % understanding of each Attribute, based on the explanation that this would help to focus future lectures where the gaps in understanding existed (to mitigate the risk of students over estimating their % understanding in the hope of obtaining a higher academic grade).

All students, despite some having quite significant Industrial experience, estimated their initial understanding at close to 0%. At the end of the first semester students with access to the explicit requirements (2017) claimed to have an average of 55% understanding while those without (2016) had only 29% understanding. By the end of the second semester this gap virtually disappeared (67% for 2016 and 68% for 2017) while at the end of the third semester the 2016 group had achieved a very high 78% (2017 not finished at time of publication).

The sample size of eleven completed workbooks is too small to draw definitive conclusions from, but they are adequate to provide early indications and direct further work. Even though the DIVOM Attributes may provide an ideal framework for an expert they are extremely intimidating *Form* for novices. This may go a long way to explaining the behavior of the specialists in the case studies. Detailed requirements which further explain the Attributes rapidly increase the user's perception of their understanding of the Attributes. They are very useful for reducing the intimidation factor which was observed during the case studies.

If the detailed requirements were provided as pre-reading to the attendees of an OSE Optimization workshop it may enable them to inform themselves prior to the workshop and minimize the intimidation factor. Because these requirements are at the lower levels of Bloom's Taxonomy [24] they could easily be tested with tools such as Moodle quizzes and the student provided with a novice level certification prior to attendance. This has the potential to create a process which transcends discipline and the collaboration issues which they cause. In this scenario the *Trans-Attribute* collaboration can be enabled where *team members must be competent enough in their own Attributes and understand the language of all relevant Attributes that enables them to contribute to the members' quality research or learning and combine various perspectives to build up a new framework* [23].

This stage of the research clearly highlights that if true understanding of the Attributes is required a significant amount of time must be invested (second and third semester) to achieve the 70-20-10 rule model [25] for learning. The dialog between a tutor and student, involving several of the common alternatives is also required to produce significantly more understanding [26] than a simplistic exposition of the correct information [27]. But that is only to be expected on the journey from Novice to Expert [28].

Regarding *Fit*, gloves normally come in pairs and The OSE Calculator and DIVOM Method is the second glove which compliments the first; the Scorecard Methodology. As with gloves, one is of limited use and the whole (The ALIZA Process) it much greater than the sum of the parts (Scorecard Methodology) + (The OSE Calculator and DIVOM Method).
